model {

	for(i in 1:n) {
	      y[i] ~ dbern(pi[i])
	      logit(pi[i]) <- inprod(x[i,], beta[])

	      # missing values
	      x[i, 4] ~ dnorm(mu1, tau1)
	      x[i, 5] ~ dbern(p)
	}

	mu1 ~ dnorm(0, .0001)
	sigma1 ~ dunif(0, 200)
	tau1 <- sigma1^-2
	p ~ dunif(0, 1)

	# priors
	# K is number of parameters to estimate
	# m is the prior mean for each parameter
	# prec is the prior precision for each parameter
	for (j in 1:K) {
		beta[j] ~ dnorm(m[j], prec[j])
	}

}